Update README.md

README.md

@@ -32,6 +32,28 @@ configs:
 **File Format:** Parquet  
 **Compression:** Snappy
 
+If you use this dataset, please cite our EMNLP 2024 paper:
+```
+@inproceedings{everaert-etal-2024-amazonqac,
+    title = "{A}mazon{QAC}: A Large-Scale, Naturalistic Query Autocomplete Dataset",
+    author = "Everaert, Dante  and
+      Patki, Rohit  and
+      Zheng, Tianqi  and
+      Potts, Christopher",
+    editor = "Dernoncourt, Franck  and
+      Preo{\c{t}}iuc-Pietro, Daniel  and
+      Shimorina, Anastasia",
+    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, US",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.emnlp-industry.78",
+    pages = "1046--1055",
+    abstract = "Query Autocomplete (QAC) is a critical feature in modern search engines, facilitating user interaction by predicting search queries based on input prefixes. Despite its widespread adoption, the absence of large-scale, realistic datasets has hindered advancements in QAC system development. This paper addresses this gap by introducing AmazonQAC, a new QAC dataset sourced from Amazon Search logs, comprising 395M samples. The dataset includes actual sequences of user-typed prefixes leading to final search terms, as well as session IDs and timestamps that support modeling the context-dependent aspects of QAC. We assess Prefix Trees, semantic retrieval, and Large Language Models (LLMs) with and without finetuning. We find that finetuned LLMs perform best, particularly when incorporating contextual information. However, even our best system achieves only half of what we calculate is theoretically possible on our test data, which implies QAC is a challenging problem that is far from solved with existing systems. This contribution aims to stimulate further research on QAC systems to better serve user needs in diverse environments. We open-source this data on Hugging Face at https://huggingface.co/datasets/amazon/AmazonQAC.",
+}
+```
+
 ## Dataset Summary
 
 AmazonQAC is a large-scale dataset designed for Query Autocomplete (QAC) tasks, sourced from real-world Amazon Search logs. It provides anonymized sequences of user-typed prefixes leading to final search terms, along with rich session metadata such as timestamps and session IDs. This dataset supports research on context-aware query completion by offering realistic, large-scale, and natural user behavior data.